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Social Indicators Research

, Volume 135, Issue 1, pp 1–14 | Cite as

A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms

  • Oscar ClaveriaEmail author
  • Enric Monte
  • Salvador Torra
Article

Abstract

In this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents’ expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.

Keywords

Economic indicators Survey-based indicators Tendency surveys Symbolic regression Evolutionary algorithms Genetic programming 

Notes

Acknowledgements

This paper has been partially financed by the project ECO2016-75805-R of the Spanish Ministry of Economy and Competitiveness. We would like to thank two anonymous referees for their useful comments and suggestions. We also wish to thank Johanna Garnitz and Klaus Wohlrabe at the Ifo Institute for Economic Research in Munich for providing us the data used in the study.

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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.AQR-IREA (Regional Quantitative Analysis Group)University of Barcelona (UB)BarcelonaSpain
  2. 2.Department of Signal Theory and CommunicationsPolytechnic University of Catalunya (UPC)BarcelonaSpain
  3. 3.Department of Econometrics and Statistics, Riskcenter-IREAUniversity of Barcelona (UB)BarcelonaSpain

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